KServe

Kubernetes InferenceService platform for predictive and generative model serving.

Provider
default
Category
Compute
Also known as
KFServing
Properties · 3 set by this component
propertytypevaluesrequiredkind defaultthis component
acceleratorenumnone | gpu | asicnononeinherited
acceleratorModel · advancedenumnvidia-h100 | nvidia-h200 | nvidia-b200 | nvidia-l4 | nvidia-a100 | amd-mi300x | aws-trainium | aws-inferentia | google-tpu | intel-gaudi | groq-lpu | cerebras-wseno—inherited
canaryRollout · component propertyboolean—nofalseinherited
deploymentMode · component propertyenumserverless | raw-deploymentnoserverlessinherited
embeddingSyncMinutes · advancedinteger—no—inherited
inputsenumlive | historical | bothnolive▸ live
instancesinteger—no—inherited
managedboolean—nofalse▸ false
modelFramework · component propertyenumtensorflow | pytorch | sklearn | xgboost | onnx | huggingfaceno—inherited
scaleToZero · component propertyboolean—notrueinherited
snapshotToObjectStore · advancedboolean—nofalseinherited
trainingenumbatch | onlinenobatch▸ batch
Rules that apply · 1

Every rule whose selector covers this component's kind or its category. Whether a rule fires depends on the design: its properties, its edges and its groups.

stateful-node-without-zone-redundancy

A stateful compute node pinned to exactly one availability zone disappears with that zone, and its state goes with it. Nodes inside a cell are exempt, because there the cell is deliberately single-zone and redundancy comes from the other cells.

  • — all of these hold:
  • — the node's `stateless` is false
  • — the node sits inside exactly 1 availability-zone group
  • — this does not hold:
  • — the node sits inside at least one cell group
Use it in a design
nodes:
  - { id: "kserve", component: "kserve" }
Other ML inference products · 37
Source
# yaml-language-server: $schema=../../schema/component.schema.json
# KServe: Kubernetes InferenceService platform for predictive and generative model serving.
# Facts from the KServe project README: Serverless mode on Knative gives request-based autoscaling
# with scale to zero and canary rollouts; RawDeployment is lighter but has neither.
schemaVersion: "1"
type: "component"
id: "kserve"
kind: "ml-inference"
provider: "default"
docs: "https://github.com/kserve/kserve"
aliases: ["KFServing"]
defaults:
  training: "batch"
  inputs: "live"
  managed: false
properties:
  deploymentMode:
    type: "enum"
    values: ["serverless", "raw-deployment"]
    default: "serverless"
  scaleToZero:
    type: "boolean"
    default: true
  canaryRollout:
    type: "boolean"
    default: false
  modelFramework:
    type: "enum"
    values: ["tensorflow", "pytorch", "sklearn", "xgboost", "onnx", "huggingface"]

The same page as markdown, for an agent: /catalogue/components/kserve.md